library(dplyr)
library(tidyr)
library(ggplot2)
library(purrr)
library(ordinal)
library(dagitty)
library(broom)
library(gganimate)
library(cowplot)
library(multiverse)
knit_as_emar()
In this paper, we investigate the relationship between social media usage in adolescents and depression, using a multiverse analysis [@Steegen2016]. Our findings suggest that the relationship is inconclusive, as the effect of social media usage regressed on self-reported ratings of depression appears to be possibly both positive and negative, depending on arbitrary choices in the data analysis process.
With the growing use of digital technology use, especially by younger, impressionable individuals, its impact on their well-being is of growing concern. Recent work has suggested that there may be an small, negative association between digital technology use and adolescent well-being [@Orben2019]. In this work, we focus specifically on usage of social media and its potential impact on mental well-being. Frequent social media usage can expose adolescents to toxic content, which is abundant on online platforms [@Vogel2021]. For example, Instagram has many posts with ``pro-eating disorder’’ content which it has struggled to moderate [@Chancellor2016, @Chancellor2016].
To better assess the harms of social media usage on adolescents, we conduct a survey with N = 300 participants and investigate the association between social media usage and depression using a multiverse analysis. We report our findings below.
Our survey questionnaire consists of 10 items which includes the following:
social_media = readRDS("../../mvis/data/social_media.rds")
head(social_media)
## # A tibble: 6 × 11
## .id parent_i…¹ age sibli…² has_s…³ depre…⁴ physi…⁵ I1 I2 I3 I4
## <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 99000 14 12 1 4 4 19 3 116 2
## 2 2 101000 14 14 1 4 2 15 2 93 2
## 3 3 88000 13 13 1 2 4 15 3 52 1
## 4 4 83000 15 13 1 3 4 21 4 74 1
## 5 5 99000 14 14 1 4 4 20 4 108 4
## 6 6 63000 14 12 1 2 7 12 2 63 3
## # … with abbreviated variable names ¹parent_income, ²sibling_age,
## # ³has_siblings, ⁴depression, ⁵physical_activity
M = multiverse()
We plan to use a linear regression model with depression as the dependent variables.
As the data was collected through a survey, we may expect there to be some outliers. We consider four alternatives for outlier_exclusion : - analysing all cases (no exclusion) - cutting off values which are 2.5 SD from the mean - cutting off values which are 3.5 SD from the mean - cutting off values based on Tukey’s fences (first and third quartiles \(\pm\) 1.5 times the interquartile range)
The results in the current analysis reflect
df = social_media
IQR.I1 = quantile(df$I1, probs = 0.75) - quantile(df$I1, probs = 0.25)
IQR.I2 = quantile(df$I2, probs = 0.75) - quantile(df$I2, probs = 0.25)
IQR.I3 = quantile(df$I3, probs = 0.75) - quantile(df$I3, probs = 0.25)
IQR.I4 = quantile(df$I4, probs = 0.75) - quantile(df$I4, probs = 0.25)
df = df %>%
filter(TRUE) %>%
mutate(I1 = scale(I1), I2 = scale(I2), I3 = scale(I3), I4 = scale(I4), )
df = social_media
IQR.I1 = quantile(df$I1, probs = 0.75) - quantile(df$I1, probs = 0.25)
IQR.I2 = quantile(df$I2, probs = 0.75) - quantile(df$I2, probs = 0.25)
IQR.I3 = quantile(df$I3, probs = 0.75) - quantile(df$I3, probs = 0.25)
IQR.I4 = quantile(df$I4, probs = 0.75) - quantile(df$I4, probs = 0.25)
df = df %>%
filter((((I1 >= (quantile(I1, probs = 0.25) - 1.5 * IQR.I1)) & (I1 < (quantile(I1,
probs = 0.75) + 1.5 * IQR.I1))) & ((I2 >= (quantile(I2, probs = 0.25) - 1.5 *
IQR.I2)) & (I2 < (quantile(I2, probs = 0.75) + 1.5 * IQR.I2))) & ((I3 >=
(quantile(I3, probs = 0.25) - 1.5 * IQR.I3)) & (I3 < (quantile(I3, probs = 0.75) +
1.5 * IQR.I3))) & ((I4 >= (quantile(I4, probs = 0.25) - 1.5 * IQR.I4)) &
(I4 < (quantile(I4, probs = 0.75) + 1.5 * IQR.I4))))) %>%
mutate(I1 = scale(I1), I2 = scale(I2), I3 = scale(I3), I4 = scale(I4), )
df = social_media
IQR.I1 = quantile(df$I1, probs = 0.75) - quantile(df$I1, probs = 0.25)
IQR.I2 = quantile(df$I2, probs = 0.75) - quantile(df$I2, probs = 0.25)
IQR.I3 = quantile(df$I3, probs = 0.75) - quantile(df$I3, probs = 0.25)
IQR.I4 = quantile(df$I4, probs = 0.75) - quantile(df$I4, probs = 0.25)
df = df %>%
filter((((I1 > (mean(I1) - 2.5 * sd(I1))) & (I1 < (mean(I1) + 2.5 * sd(I1)))) &
((I2 > (mean(I2) - 2.5 * sd(I2))) & (I2 < (mean(I2) + 2.5 * sd(I2)))) & ((I3 >
(mean(I3) - 2.5 * sd(I3))) & (I3 < (mean(I3) + 2.5 * sd(I3)))) & ((I4 > (mean(I4) -
2.5 * sd(I4))) & (I4 < (mean(I4) + 2.5 * sd(I4)))))) %>%
mutate(I1 = scale(I1), I2 = scale(I2), I3 = scale(I3), I4 = scale(I4), )
df = social_media
IQR.I1 = quantile(df$I1, probs = 0.75) - quantile(df$I1, probs = 0.25)
IQR.I2 = quantile(df$I2, probs = 0.75) - quantile(df$I2, probs = 0.25)
IQR.I3 = quantile(df$I3, probs = 0.75) - quantile(df$I3, probs = 0.25)
IQR.I4 = quantile(df$I4, probs = 0.75) - quantile(df$I4, probs = 0.25)
df = df %>%
filter((((I1 > (mean(I1) - 3.5 * sd(I1))) & (I1 < (mean(I1) + 3.5 * sd(I1)))) &
((I2 > (mean(I2) - 3.5 * sd(I2))) & (I2 < (mean(I2) + 3.5 * sd(I2)))) & ((I3 >
(mean(I3) - 3.5 * sd(I3))) & (I3 < (mean(I3) + 3.5 * sd(I3)))) & ((I4 > (mean(I4) -
3.5 * sd(I4))) & (I4 < (mean(I4) + 3.5 * sd(I4)))))) %>%
mutate(I1 = scale(I1), I2 = scale(I2), I3 = scale(I3), I4 = scale(I4), )
df = social_media
IQR.I1 = quantile(df$I1, probs = 0.75) - quantile(df$I1, probs = 0.25)
IQR.I2 = quantile(df$I2, probs = 0.75) - quantile(df$I2, probs = 0.25)
IQR.I3 = quantile(df$I3, probs = 0.75) - quantile(df$I3, probs = 0.25)
IQR.I4 = quantile(df$I4, probs = 0.75) - quantile(df$I4, probs = 0.25)
df = df %>%
filter(branch(
outlier_exclusion,
"no_exclusion" ~ TRUE,
"one-half_interquartile_range" ~ (
((I1 >= (quantile(I1, probs = 0.25) - 1.5 * IQR.I1)) & (I1 < (quantile(I1, probs = 0.75) + 1.5*IQR.I1))) &
((I2 >= (quantile(I2, probs = 0.25) - 1.5 * IQR.I2)) & (I2 < (quantile(I2, probs = 0.75) + 1.5*IQR.I2))) &
((I3 >= (quantile(I3, probs = 0.25) - 1.5 * IQR.I3)) & (I3 < (quantile(I3, probs = 0.75) + 1.5*IQR.I3))) &
((I4 >= (quantile(I4, probs = 0.25) - 1.5 * IQR.I4)) & (I4 < (quantile(I4, probs = 0.75) + 1.5*IQR.I4)))
),
"two-half_sd_from_mean" ~ (
((I1 > (mean(I1) - 2.5 * sd(I1))) & (I1 < (mean(I1) + 2.5 * sd(I1)))) &
((I2 > (mean(I2) - 2.5 * sd(I2))) & (I2 < (mean(I2) + 2.5 * sd(I2)))) &
((I3 > (mean(I3) - 2.5 * sd(I3))) & (I3 < (mean(I3) + 2.5 * sd(I3)))) &
((I4 > (mean(I4) - 2.5 * sd(I4))) & (I4 < (mean(I4) + 2.5 * sd(I4))))
),
"three-half_sd_from_mean" ~ (
((I1 > (mean(I1) - 3.5 * sd(I1))) & (I1 < (mean(I1) + 3.5 * sd(I1)))) &
((I2 > (mean(I2) - 3.5 * sd(I2))) & (I2 < (mean(I2) + 3.5 * sd(I2)))) &
((I3 > (mean(I3) - 3.5 * sd(I3))) & (I3 < (mean(I3) + 3.5 * sd(I3)))) &
((I4 > (mean(I4) - 3.5 * sd(I4))) & (I4 < (mean(I4) + 3.5 * sd(I4))))
)
)) %>%
mutate(
I1 = scale(I1),
I2 = scale(I2),
I3 = scale(I3),
I4 = scale(I4),
)
We consider the inclusion/exclusion of three covariates:
parental_income as a covariate will likely
decrease precision, and thus we do not include this variable in our
regression.
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + NULL + physical_activity + factor(has_siblings),
data = df)
fit = lm(depression ~ social_media_usage + age + NULL + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + NULL + factor(has_siblings), data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + NULL, data = df)
fit = lm(depression ~ social_media_usage + age + physical_activity + factor(has_siblings),
data = df)
fit = lm(
depression ~ social_media_usage +
branch(age_covariate, "not_included" ~ NULL, "included" ~ age) +
branch(activity_covariate, "not_included" ~ NULL, "included" ~ physical_activity) +
branch(sibling_covariate, "not_included" ~ NULL, "included" ~ factor(has_siblings)),
data = df
)
Below, we show the estimated coefficients of our regresion model
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.93 0.0699 41.9 7.37e-127 2.79 3.06
2 social_media_usage 0.197 0.0700 2.82 5.20e- 3 0.0593 0.335
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.22 0.131 24.6 1.62e-73 2.96 3.48
2 social_media_usage 0.185 0.0695 2.66 8.13e- 3 0.0484 0.322
3 factor(has_siblings)1 -0.406 0.154 -2.62 9.11e- 3 -0.710 -0.101
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.62 0.115 40.2 3.24e-122 4.39 4.84
2 social_media_usage -0.0327 0.0527 -0.621 5.35e- 1 -0.136 0.0710
3 physical_activity -0.459 0.0280 -16.4 1.67e- 43 -0.514 -0.404
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.58 0.128 35.7 2.75e-109 4.33 4.83
2 social_media_usage -0.0329 0.0527 -0.624 5.33e- 1 -0.137 0.0709
3 physical_activity -0.464 0.0290 -16.0 5.61e- 42 -0.521 -0.407
4 factor(has_siblings)1 0.0773 0.117 0.659 5.10e- 1 -0.153 0.308
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.66 0.927 5.03 0.000000867 2.83 6.48
2 social_media_usage 0.223 0.0711 3.14 0.00185 0.0835 0.363
3 age -0.127 0.0676 -1.87 0.0619 -0.260 0.00634
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.84 0.921 5.26 0.000000283 3.03 6.65
2 social_media_usage 0.210 0.0707 2.98 0.00316 0.0713 0.349
3 age -0.119 0.0670 -1.78 0.0761 -0.251 0.0126
4 factor(has_siblings)1 -0.394 0.154 -2.56 0.0111 -0.697 -0.0905
# … with abbreviated variable names ¹conf.low, ²conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.76 0.677 8.51 8.56e-16 4.43 7.09
2 social_media_usage -0.0139 0.0537 -0.259 7.96e- 1 -0.119 0.0917
3 age -0.0844 0.0491 -1.72 8.69e- 2 -0.181 0.0123
4 physical_activity -0.456 0.0279 -16.3 3.04e-43 -0.511 -0.401
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.73 0.678 8.46 1.29e-15 4.40 7.07
2 social_media_usage -0.0139 0.0537 -0.258 7.97e- 1 -0.120 0.0918
3 age -0.0855 0.0492 -1.74 8.34e- 2 -0.182 0.0114
4 physical_activity -0.462 0.0289 -16.0 8.23e-42 -0.518 -0.405
5 factor(has_siblings)1 0.0835 0.117 0.714 4.76e- 1 -0.147 0.314
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.93 0.0704 41.6 3.97e-126 2.79 3.07
2 social_media_usage 0.139 0.0705 1.98 4.91e- 2 0.000542 0.278
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.25 0.131 24.7 5.63e-74 2.99 3.51
2 social_media_usage 0.146 0.0697 2.10 3.65e- 2 0.00928 0.284
3 factor(has_siblings)1 -0.444 0.155 -2.87 4.44e- 3 -0.749 -0.139
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.60 0.113 40.8 8.75e-124 4.38 4.82
2 social_media_usage 0.000872 0.0515 0.0169 9.86e- 1 -0.100 0.102
3 physical_activity -0.454 0.0273 -16.6 2.82e- 44 -0.508 -0.400
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.56 0.126 36.2 8.70e-111 4.31 4.81
2 social_media_usage -0.00194 0.0517 -0.0375 9.70e- 1 -0.104 0.0999
3 physical_activity -0.459 0.0285 -16.1 1.83e- 42 -0.515 -0.403
4 factor(has_siblings)1 0.0773 0.118 0.657 5.12e- 1 -0.154 0.309
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.42 0.928 4.76 0.00000308 2.59 6.24
2 social_media_usage 0.158 0.0712 2.21 0.0275 0.0176 0.298
3 age -0.109 0.0677 -1.61 0.109 -0.242 0.0243
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.67 0.922 5.07 0.000000717 2.86 6.48
2 social_media_usage 0.164 0.0704 2.33 0.0205 0.0255 0.303
3 age -0.104 0.0669 -1.56 0.120 -0.236 0.0273
4 factor(has_siblings)1 -0.439 0.155 -2.84 0.00488 -0.743 -0.134
# … with abbreviated variable names ¹conf.low, ²conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.82 0.674 8.63 3.76e-16 4.49 7.14
2 social_media_usage 0.0165 0.0520 0.316 7.52e- 1 -0.0859 0.119
3 age -0.0895 0.0488 -1.84 6.73e- 2 -0.185 0.00643
4 physical_activity -0.453 0.0272 -16.6 2.65e-44 -0.507 -0.399
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.79 0.676 8.56 6.23e-16 4.46 7.12
2 social_media_usage 0.0136 0.0522 0.261 7.95e- 1 -0.0891 0.116
3 age -0.0901 0.0488 -1.85 6.58e- 2 -0.186 0.00593
4 physical_activity -0.458 0.0283 -16.2 1.54e-42 -0.514 -0.403
5 factor(has_siblings)1 0.0811 0.117 0.692 4.89e- 1 -0.150 0.312
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.93 0.0708 41.4 1.69e-125 2.79 3.07
2 social_media_usage -0.0491 0.0709 -0.693 4.89e- 1 -0.189 0.0904
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.24 0.132 24.5 2.65e-73 2.98 3.50
2 social_media_usage -0.0594 0.0702 -0.846 3.98e- 1 -0.197 0.0787
3 factor(has_siblings)1 -0.439 0.156 -2.82 5.18e- 3 -0.746 -0.132
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.60 0.111 41.4 2.63e-125 4.38 4.82
2 social_media_usage -0.0663 0.0507 -1.31 1.92e- 1 -0.166 0.0335
3 physical_activity -0.455 0.0269 -16.9 2.20e- 45 -0.508 -0.402
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.57 0.125 36.5 1.41e-111 4.32 4.81
2 social_media_usage -0.0648 0.0508 -1.28 2.03e- 1 -0.165 0.0352
3 physical_activity -0.459 0.0279 -16.4 1.29e- 43 -0.514 -0.404
4 factor(has_siblings)1 0.0697 0.117 0.595 5.52e- 1 -0.161 0.300
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.08 0.923 4.42 0.0000141 2.26 5.89
2 social_media_usage -0.0480 0.0708 -0.678 0.498 -0.187 0.0914
3 age -0.0841 0.0673 -1.25 0.212 -0.216 0.0483
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.31 0.917 4.71 0.00000390 2.51 6.12
2 social_media_usage -0.0582 0.0701 -0.830 0.407 -0.196 0.0798
3 age -0.0786 0.0666 -1.18 0.239 -0.210 0.0524
4 factor(has_siblings)1 -0.434 0.156 -2.78 0.00575 -0.741 -0.127
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.78 0.666 8.68 2.66e-16 4.47 7.09
2 social_media_usage -0.0651 0.0505 -1.29 1.98e- 1 -0.165 0.0343
3 age -0.0862 0.0480 -1.80 7.33e- 2 -0.181 0.00818
4 physical_activity -0.455 0.0268 -17.0 1.34e-45 -0.508 -0.402
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.76 0.667 8.63 4.00e-16 4.44 7.07
2 social_media_usage -0.0635 0.0506 -1.26 2.10e- 1 -0.163 0.0361
3 age -0.0872 0.0480 -1.82 7.04e- 2 -0.182 0.00732
4 physical_activity -0.460 0.0278 -16.5 6.93e-44 -0.515 -0.405
5 factor(has_siblings)1 0.0764 0.117 0.655 5.13e- 1 -0.153 0.306
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.93 0.0707 41.4 1.42e-125 2.79 3.07
2 social_media_usage -0.0669 0.0708 -0.945 3.46e- 1 -0.206 0.0725
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.23 0.133 24.4 6.81e-73 2.97 3.49
2 social_media_usage -0.0527 0.0703 -0.750 4.54e- 1 -0.191 0.0856
3 factor(has_siblings)1 -0.424 0.156 -2.71 7.08e- 3 -0.731 -0.116
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.61 0.111 41.5 9.31e-126 4.39 4.82
2 social_media_usage -0.0986 0.0505 -1.95 5.20e- 2 -0.198 0.000840
3 physical_activity -0.456 0.0268 -17.0 9.50e- 46 -0.509 -0.403
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.56 0.125 36.6 7.12e-112 4.31 4.80
2 social_media_usage -0.102 0.0508 -2.02 4.47e- 2 -0.202 -0.00242
3 physical_activity -0.462 0.0279 -16.6 3.57e- 44 -0.517 -0.408
4 factor(has_siblings)1 0.0975 0.117 0.834 4.05e- 1 -0.133 0.328
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.02 0.926 4.34 0.0000198 2.19 5.84
2 social_media_usage -0.0596 0.0711 -0.839 0.402 -0.199 0.0802
3 age -0.0797 0.0675 -1.18 0.239 -0.213 0.0531
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.26 0.921 4.63 0.00000553 2.45 6.08
2 social_media_usage -0.0459 0.0705 -0.652 0.515 -0.185 0.0928
3 age -0.0756 0.0668 -1.13 0.259 -0.207 0.0559
4 factor(has_siblings)1 -0.420 0.156 -2.69 0.00762 -0.727 -0.112
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.69 0.666 8.54 6.96e-16 4.38 7.00
2 social_media_usage -0.0913 0.0506 -1.81 7.20e- 2 -0.191 0.00821
3 age -0.0795 0.0480 -1.65 9.91e- 2 -0.174 0.0151
4 physical_activity -0.456 0.0267 -17.1 6.83e-46 -0.509 -0.403
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.66 0.668 8.47 1.17e-15 4.34 6.97
2 social_media_usage -0.0951 0.0508 -1.87 6.21e- 2 -0.195 0.00483
3 age -0.0805 0.0481 -1.67 9.52e- 2 -0.175 0.0141
4 physical_activity -0.463 0.0278 -16.7 2.32e-44 -0.517 -0.408
5 factor(has_siblings)1 0.102 0.117 0.876 3.82e- 1 -0.127 0.332
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.93 0.0698 42.0 4.55e-127 2.79 3.06
2 social_media_usage 0.132 0.0438 3.01 2.80e- 3 0.0458 0.218
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.23 0.130 24.8 2.88e-74 2.98 3.49
2 social_media_usage 0.130 0.0433 3.00 2.97e- 3 0.0445 0.215
3 factor(has_siblings)1 -0.424 0.154 -2.76 6.17e- 3 -0.726 -0.121
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.61 0.115 40.1 5.73e-122 4.38 4.83
2 social_media_usage -0.0125 0.0330 -0.378 7.05e- 1 -0.0775 0.0525
3 physical_activity -0.457 0.0280 -16.3 3.32e- 43 -0.512 -0.402
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.57 0.128 35.8 1.56e-109 4.32 4.82
2 social_media_usage -0.0138 0.0331 -0.416 6.78e- 1 -0.0789 0.0514
3 physical_activity -0.462 0.0291 -15.9 1.56e- 41 -0.520 -0.405
4 factor(has_siblings)1 0.0797 0.117 0.678 4.98e- 1 -0.152 0.311
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.80 0.930 5.16 0.000000460 2.97 6.63
2 social_media_usage 0.152 0.0447 3.41 0.000751 0.0642 0.240
3 age -0.137 0.0678 -2.01 0.0448 -0.270 -0.00318
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.h…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.01 0.924 5.42 1.22e-7 3.19 6.83
2 social_media_usage 0.149 0.0442 3.37 8.50e-4 0.0620 0.236
3 age -0.130 0.0671 -1.94 5.30e-2 -0.262 0.00170
4 factor(has_siblings)1 -0.414 0.153 -2.70 7.26e-3 -0.715 -0.113
# … with abbreviated variable names ¹conf.low, ²conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.79 0.680 8.52 8.25e-16 4.46 7.13
2 social_media_usage 0.00144 0.0338 0.0425 9.66e- 1 -0.0652 0.0680
3 age -0.0875 0.0495 -1.77 7.80e- 2 -0.185 0.00986
4 physical_activity -0.454 0.0279 -16.2 7.23e-43 -0.509 -0.399
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.76 0.682 8.45 1.35e-15 4.42 7.11
2 social_media_usage 0.000214 0.0339 0.00631 9.95e- 1 -0.0665 0.0670
3 age -0.0881 0.0495 -1.78 7.61e- 2 -0.186 0.00931
4 physical_activity -0.460 0.0290 -15.8 2.85e-41 -0.517 -0.402
5 factor(has_siblings)1 0.0835 0.117 0.713 4.76e- 1 -0.147 0.314
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.93 0.0706 41.5 9.11e-126 2.79 3.07
2 social_media_usage 0.0440 0.0316 1.39 1.65e- 1 -0.0182 0.106
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.24 0.132 24.5 2.02e-73 2.98 3.50
2 social_media_usage 0.0437 0.0313 1.40 1.63e- 1 -0.0178 0.105
3 factor(has_siblings)1 -0.432 0.155 -2.78 5.83e- 3 -0.738 -0.126
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.64 0.113 41.0 2.48e-124 4.41 4.86
2 social_media_usage -0.0407 0.0232 -1.76 7.96e- 2 -0.0863 0.00484
3 physical_activity -0.465 0.0275 -16.9 2.28e- 45 -0.519 -0.411
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.59 0.126 36.4 2.12e-111 4.35 4.84
2 social_media_usage -0.0417 0.0232 -1.80 7.31e- 2 -0.0874 0.00393
3 physical_activity -0.471 0.0286 -16.5 1.08e- 43 -0.527 -0.414
4 factor(has_siblings)1 0.0890 0.117 0.762 4.47e- 1 -0.141 0.319
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.41 0.939 4.69 0.00000408 2.56 6.26
2 social_media_usage 0.0545 0.0322 1.69 0.0919 -0.00892 0.118
3 age -0.108 0.0685 -1.58 0.114 -0.243 0.0264
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.64 0.933 4.97 0.00000113 2.80 6.47
2 social_media_usage 0.0536 0.0319 1.68 0.0933 -0.00906 0.116
3 age -0.103 0.0678 -1.52 0.131 -0.236 0.0306
4 factor(has_siblings)1 -0.425 0.155 -2.74 0.00659 -0.730 -0.119
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.62 0.676 8.32 3.31e-15 4.29 6.95
2 social_media_usage -0.0334 0.0236 -1.41 1.58e- 1 -0.0799 0.0131
3 age -0.0725 0.0490 -1.48 1.40e- 1 -0.169 0.0240
4 physical_activity -0.463 0.0275 -16.9 3.73e-45 -0.517 -0.409
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.59 0.678 8.25 5.44e-15 4.25 6.92
2 social_media_usage -0.0344 0.0237 -1.45 1.47e- 1 -0.0810 0.0122
3 age -0.0732 0.0491 -1.49 1.37e- 1 -0.170 0.0233
4 physical_activity -0.469 0.0285 -16.4 1.54e-43 -0.525 -0.413
5 factor(has_siblings)1 0.0924 0.117 0.792 4.29e- 1 -0.137 0.322
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.97 0.0732 40.5 2.79e-115 2.82 3.11
2 social_media_usage 0.275 0.0734 3.75 2.18e- 4 0.131 0.420
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.21 0.137 23.4 2.46e-66 2.94 3.48
2 social_media_usage 0.266 0.0730 3.64 3.30e- 4 0.122 0.409
3 factor(has_siblings)1 -0.346 0.162 -2.13 3.37e- 2 -0.665 -0.0268
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.68 0.124 37.7 4.10e-108 4.44 4.93
2 social_media_usage 0.0118 0.0562 0.209 8.34e- 1 -0.0988 0.122
3 physical_activity -0.471 0.0308 -15.3 2.85e- 38 -0.532 -0.411
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.64 0.138 33.5 8.99e-97 4.37 4.91
2 social_media_usage 0.0113 0.0562 0.200 8.41e- 1 -0.0994 0.122
3 physical_activity -0.476 0.0317 -15.0 3.05e-37 -0.539 -0.414
4 factor(has_siblings)1 0.0794 0.122 0.649 5.17e- 1 -0.161 0.320
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.83 0.975 4.96 0.00000129 2.91 6.75
2 social_media_usage 0.302 0.0743 4.06 0.0000645 0.155 0.448
3 age -0.136 0.0711 -1.92 0.0560 -0.276 0.00352
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.h…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.05 0.974 5.19 4.26e-7 3.14 6.97
2 social_media_usage 0.292 0.0740 3.95 1.01e-4 0.146 0.438
3 age -0.135 0.0706 -1.91 5.76e-2 -0.274 0.00438
4 factor(has_siblings)1 -0.342 0.161 -2.12 3.48e-2 -0.659 -0.0247
# … with abbreviated variable names ¹conf.low, ²conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.45 0.714 9.02 4.03e-17 5.04 7.85
2 social_media_usage 0.0374 0.0565 0.662 5.09e- 1 -0.0739 0.149
3 age -0.129 0.0515 -2.51 1.28e- 2 -0.231 -0.0277
4 physical_activity -0.471 0.0305 -15.4 1.08e-38 -0.531 -0.411
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.41 0.717 8.94 7.40e-17 5.00 7.82
2 social_media_usage 0.0370 0.0566 0.654 5.14e- 1 -0.0745 0.148
3 age -0.130 0.0516 -2.51 1.26e- 2 -0.231 -0.0280
4 physical_activity -0.476 0.0314 -15.2 1.11e-37 -0.537 -0.414
5 factor(has_siblings)1 0.0824 0.121 0.681 4.97e- 1 -0.156 0.321
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.97 0.0741 40.0 4.12e-114 2.82 3.11
2 social_media_usage 0.204 0.0742 2.75 6.42e- 3 0.0578 0.350
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.25 0.138 23.5 1.45e-66 2.98 3.52
2 social_media_usage 0.210 0.0736 2.85 4.68e- 3 0.0650 0.355
3 factor(has_siblings)1 -0.397 0.163 -2.43 1.57e- 2 -0.718 -0.0754
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.69 0.122 38.4 8.77e-110 4.45 4.93
2 social_media_usage -0.00744 0.0551 -0.135 8.93e- 1 -0.116 0.101
3 physical_activity -0.474 0.0302 -15.7 1.27e- 39 -0.534 -0.415
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.65 0.136 34.2 1.14e-98 4.39 4.92
2 social_media_usage -0.0110 0.0554 -0.199 8.42e- 1 -0.120 0.0981
3 physical_activity -0.480 0.0313 -15.3 2.62e-38 -0.541 -0.418
4 factor(has_siblings)1 0.0821 0.123 0.668 5.05e- 1 -0.160 0.324
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.66 0.988 4.72 0.00000391 2.71 6.61
2 social_media_usage 0.229 0.0753 3.03 0.00265 0.0803 0.377
3 age -0.124 0.0720 -1.72 0.0867 -0.266 0.0180
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.96 0.987 5.03 0.000000917 3.02 6.91
2 social_media_usage 0.235 0.0747 3.14 0.00186 0.0878 0.382
3 age -0.125 0.0714 -1.75 0.0811 -0.266 0.0156
4 factor(has_siblings)1 -0.399 0.163 -2.45 0.0149 -0.719 -0.0784
# … with abbreviated variable names ¹conf.low, ²conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.42 0.716 8.96 6.27e-17 5.01 7.83
2 social_media_usage 0.0175 0.0556 0.315 7.53e- 1 -0.0919 0.127
3 age -0.126 0.0516 -2.44 1.53e- 2 -0.228 -0.0244
4 physical_activity -0.475 0.0299 -15.8 3.98e-40 -0.534 -0.416
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.38 0.720 8.86 1.28e-16 4.96 7.79
2 social_media_usage 0.0139 0.0559 0.249 8.03e- 1 -0.0961 0.124
3 age -0.126 0.0516 -2.44 1.55e- 2 -0.227 -0.0241
4 physical_activity -0.480 0.0310 -15.5 8.74e-39 -0.541 -0.419
5 factor(has_siblings)1 0.0804 0.122 0.661 5.09e- 1 -0.159 0.320
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.97 0.0750 39.6 5.66e-113 2.82 3.11
2 social_media_usage 0.0868 0.0751 1.16 2.49e- 1 -0.0611 0.235
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.23 0.141 22.9 1.51e-64 2.95 3.50
2 social_media_usage 0.0667 0.0752 0.887 3.76e- 1 -0.0813 0.215
3 factor(has_siblings)1 -0.363 0.167 -2.18 3.02e- 2 -0.691 -0.0349
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.70 0.120 39.1 1.24e-111 4.46 4.93
2 social_media_usage -0.0252 0.0539 -0.467 6.41e- 1 -0.131 0.0809
3 physical_activity -0.475 0.0295 -16.1 5.39e- 41 -0.533 -0.417
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.66 0.136 34.3 6.89e-99 4.39 4.92
2 social_media_usage -0.0220 0.0542 -0.406 6.85e- 1 -0.129 0.0847
3 physical_activity -0.479 0.0304 -15.8 6.63e-40 -0.539 -0.420
4 factor(has_siblings)1 0.0749 0.123 0.610 5.42e- 1 -0.167 0.317
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.28 0.994 4.31 0.0000235 2.32 6.24
2 social_media_usage 0.101 0.0758 1.33 0.184 -0.0482 0.250
3 age -0.0961 0.0724 -1.33 0.186 -0.239 0.0466
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.50 0.992 4.53 0.00000878 2.55 6.45
2 social_media_usage 0.0807 0.0758 1.06 0.288 -0.0686 0.230
3 age -0.0933 0.0720 -1.30 0.196 -0.235 0.0484
4 factor(has_siblings)1 -0.359 0.167 -2.16 0.0318 -0.687 -0.0314
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.37 0.714 8.93 7.67e-17 4.97 7.78
2 social_media_usage -0.00762 0.0539 -0.141 8.88e- 1 -0.114 0.0986
3 age -0.122 0.0512 -2.38 1.79e- 2 -0.223 -0.0212
4 physical_activity -0.477 0.0293 -16.3 1.09e-41 -0.535 -0.420
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.34 0.716 8.86 1.25e-16 4.93 7.75
2 social_media_usage -0.00401 0.0542 -0.0740 9.41e- 1 -0.111 0.103
3 age -0.123 0.0513 -2.40 1.72e- 2 -0.224 -0.0220
4 physical_activity -0.482 0.0301 -16.0 1.24e-40 -0.541 -0.423
5 factor(has_siblings)1 0.0824 0.122 0.677 4.99e- 1 -0.157 0.322
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.97 0.0750 39.5 7.14e-113 2.82 3.11
2 social_media_usage 0.0669 0.0752 0.890 3.74e- 1 -0.0811 0.215
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.25 0.140 23.1 2.20e-65 2.97 3.52
2 social_media_usage 0.0763 0.0747 1.02 3.07e- 1 -0.0707 0.223
3 factor(has_siblings)1 -0.390 0.166 -2.36 1.91e- 2 -0.717 -0.0643
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.70 0.120 39.2 7.59e-112 4.47 4.94
2 social_media_usage -0.0518 0.0539 -0.962 3.37e- 1 -0.158 0.0542
3 physical_activity -0.477 0.0295 -16.2 2.89e- 41 -0.535 -0.419
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.66 0.134 34.7 7.30e-100 4.39 4.92
2 social_media_usage -0.0554 0.0541 -1.02 3.07e- 1 -0.162 0.0512
3 physical_activity -0.483 0.0305 -15.8 4.85e- 40 -0.543 -0.423
4 factor(has_siblings)1 0.0909 0.122 0.742 4.59e- 1 -0.150 0.332
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.15 0.987 4.20 0.0000361 2.21 6.09
2 social_media_usage 0.0724 0.0753 0.961 0.337 -0.0758 0.221
3 age -0.0865 0.0720 -1.20 0.230 -0.228 0.0552
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.44 0.986 4.50 0.0000103 2.50 6.38
2 social_media_usage 0.0818 0.0747 1.10 0.274 -0.0653 0.229
3 age -0.0870 0.0713 -1.22 0.224 -0.228 0.0534
4 factor(has_siblings)1 -0.391 0.165 -2.36 0.0188 -0.717 -0.0652
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.36 0.708 8.99 5.20e-17 4.97 7.76
2 social_media_usage -0.0450 0.0535 -0.841 4.01e- 1 -0.150 0.0603
3 age -0.121 0.0507 -2.38 1.81e- 2 -0.221 -0.0208
4 physical_activity -0.480 0.0293 -16.4 5.05e-42 -0.538 -0.422
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.32 0.711 8.89 1.00e-16 4.92 7.72
2 social_media_usage -0.0486 0.0537 -0.905 3.66e- 1 -0.154 0.0572
3 age -0.121 0.0508 -2.38 1.79e- 2 -0.221 -0.0210
4 physical_activity -0.486 0.0303 -16.0 8.50e-41 -0.546 -0.426
5 factor(has_siblings)1 0.0931 0.121 0.767 4.44e- 1 -0.146 0.332
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.97 0.0732 40.5 2.25e-115 2.82 3.11
2 social_media_usage 0.164 0.0428 3.82 1.67e- 4 0.0793 0.248
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.23 0.137 23.7 4.63e-67 2.96 3.50
2 social_media_usage 0.162 0.0425 3.81 1.71e- 4 0.0784 0.246
3 factor(has_siblings)1 -0.372 0.161 -2.30 2.20e- 2 -0.689 -0.0539
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.69 0.125 37.6 8.49e-108 4.44 4.93
2 social_media_usage 0.00139 0.0330 0.0422 9.66e- 1 -0.0636 0.0664
3 physical_activity -0.473 0.0310 -15.3 3.74e- 38 -0.534 -0.412
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.65 0.138 33.6 4.26e-97 4.38 4.92
2 social_media_usage -0.0000325 0.0331 -0.000982 9.99e- 1 -0.0652 0.0651
3 physical_activity -0.478 0.0320 -15.0 5.77e-37 -0.541 -0.415
4 factor(has_siblings)1 0.0797 0.123 0.651 5.16e- 1 -0.162 0.321
# … with abbreviated variable names ¹conf.low, ²conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.00 0.980 5.10 0.000000642 3.07 6.93
2 social_media_usage 0.184 0.0436 4.21 0.0000355 0.0977 0.270
3 age -0.149 0.0714 -2.08 0.0384 -0.289 -0.00798
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.h…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.26 0.978 5.37 1.70e-7 3.33 7.18
2 social_media_usage 0.182 0.0433 4.20 3.61e-5 0.0967 0.267
3 age -0.148 0.0708 -2.09 3.77e-2 -0.287 -0.00849
4 factor(has_siblings)1 -0.370 0.160 -2.31 2.17e-2 -0.686 -0.0547
# … with abbreviated variable names ¹conf.low, ²conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.45 0.718 8.99 5.23e-17 5.04 7.87
2 social_media_usage 0.0195 0.0335 0.582 5.61e- 1 -0.0464 0.0853
3 age -0.130 0.0519 -2.50 1.32e- 2 -0.232 -0.0273
4 physical_activity -0.471 0.0307 -15.4 2.01e-38 -0.532 -0.411
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.41 0.721 8.89 1.03e-16 4.99 7.83
2 social_media_usage 0.0180 0.0336 0.537 5.92e- 1 -0.0481 0.0841
3 age -0.129 0.0520 -2.49 1.34e- 2 -0.232 -0.0271
4 physical_activity -0.476 0.0317 -15.0 3.15e-37 -0.539 -0.414
5 factor(has_siblings)1 0.0791 0.121 0.652 5.15e- 1 -0.160 0.318
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.97 0.0736 40.3 1.01e-114 2.82 3.11
2 social_media_usage 0.0942 0.0285 3.31 1.07e- 3 0.0381 0.150
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.23 0.138 23.4 2.69e-66 2.95 3.50
2 social_media_usage 0.0918 0.0283 3.25 1.31e- 3 0.0362 0.147
3 factor(has_siblings)1 -0.362 0.163 -2.22 2.69e- 2 -0.682 -0.0416
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.71 0.124 37.9 1.79e-108 4.46 4.95
2 social_media_usage -0.0119 0.0217 -0.547 5.85e- 1 -0.0546 0.0309
3 physical_activity -0.479 0.0309 -15.5 5.67e- 39 -0.540 -0.418
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.67 0.138 33.8 1.98e-97 4.40 4.94
2 social_media_usage -0.0125 0.0218 -0.574 5.66e- 1 -0.0553 0.0303
3 physical_activity -0.484 0.0318 -15.2 7.27e-38 -0.547 -0.421
4 factor(has_siblings)1 0.0826 0.122 0.675 5.00e- 1 -0.158 0.323
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.90 0.988 4.96 0.00000124 2.96 6.85
2 social_media_usage 0.107 0.0290 3.68 0.000282 0.0497 0.164
3 age -0.142 0.0720 -1.97 0.0503 -0.283 0.000196
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.h…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.14 0.986 5.21 3.77e-7 3.20 7.08e+0
2 social_media_usage 0.104 0.0288 3.62 3.52e-4 0.0477 1.61e-1
3 age -0.140 0.0715 -1.96 5.09e-2 -0.281 5.53e-4
4 factor(has_siblings)1 -0.359 0.162 -2.22 2.73e-2 -0.677 -4.05e-2
# … with abbreviated variable names ¹conf.low, ²conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.38 0.719 8.87 1.13e-16 4.97 7.80
2 social_media_usage -0.000458 0.0221 -0.0207 9.83e- 1 -0.0439 0.0430
3 age -0.123 0.0520 -2.36 1.89e- 2 -0.225 -0.0204
4 physical_activity -0.477 0.0306 -15.6 3.36e-39 -0.537 -0.417
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.34 0.722 8.78 2.15e-16 4.92 7.76
2 social_media_usage -0.00106 0.0221 -0.0478 9.62e- 1 -0.0446 0.0425
3 age -0.123 0.0520 -2.36 1.89e- 2 -0.225 -0.0204
4 physical_activity -0.482 0.0316 -15.3 4.22e-38 -0.544 -0.420
5 factor(has_siblings)1 0.0835 0.121 0.689 4.91e- 1 -0.155 0.322
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0720 41.1 1.54e-119 2.82 3.10
2 social_media_usage 0.251 0.0722 3.48 5.85e- 4 0.109 0.393
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.22 0.135 23.8 8.94e-69 2.95 3.48
2 social_media_usage 0.240 0.0718 3.35 9.31e- 4 0.0989 0.381
3 factor(has_siblings)1 -0.363 0.159 -2.28 2.34e- 2 -0.677 -0.0495
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.68 0.123 38.2 7.33e-112 4.44 4.93
2 social_media_usage -0.0171 0.0554 -0.308 7.58e- 1 -0.126 0.0920
3 physical_activity -0.476 0.0305 -15.6 1.20e- 39 -0.536 -0.416
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.64 0.137 34.0 2.33e-100 4.38 4.91
2 social_media_usage -0.0175 0.0555 -0.316 7.52e- 1 -0.127 0.0917
3 physical_activity -0.481 0.0315 -15.3 1.72e- 38 -0.543 -0.419
4 factor(has_siblings)1 0.0819 0.121 0.677 4.99e- 1 -0.156 0.320
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.50 0.948 4.74 0.00000342 2.63 6.36
2 social_media_usage 0.273 0.0732 3.73 0.000234 0.129 0.417
3 age -0.113 0.0693 -1.63 0.105 -0.249 0.0236
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.72 0.946 4.98 0.00000111 2.85 6.58
2 social_media_usage 0.262 0.0728 3.59 0.000386 0.118 0.405
3 age -0.110 0.0688 -1.60 0.111 -0.245 0.0255
4 factor(has_siblings)1 -0.359 0.159 -2.26 0.0249 -0.672 -0.0456
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.16 0.698 8.83 1.29e-16 4.79 7.54
2 social_media_usage 0.00418 0.0559 0.0747 9.41e- 1 -0.106 0.114
3 age -0.108 0.0504 -2.15 3.23e- 2 -0.208 -0.00921
4 physical_activity -0.475 0.0303 -15.7 6.15e-40 -0.535 -0.416
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.13 0.700 8.75 2.22e-16 4.75 7.51
2 social_media_usage 0.00381 0.0560 0.0681 9.46e- 1 -0.106 0.114
3 age -0.109 0.0505 -2.16 3.15e- 2 -0.208 -0.00974
4 physical_activity -0.481 0.0313 -15.4 8.14e-39 -0.542 -0.419
5 factor(has_siblings)1 0.0865 0.120 0.719 4.72e- 1 -0.150 0.323
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0723 40.9 4.09e-119 2.81 3.10
2 social_media_usage 0.226 0.0725 3.12 2.00e- 3 0.0834 0.369
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.26 0.135 24.1 1.08e-69 2.99 3.52
2 social_media_usage 0.232 0.0717 3.23 1.37e- 3 0.0908 0.373
3 factor(has_siblings)1 -0.415 0.159 -2.60 9.73e- 3 -0.729 -0.101
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.67 0.121 38.5 7.84e-113 4.44 4.91
2 social_media_usage 0.000624 0.0546 0.0114 9.91e- 1 -0.107 0.108
3 physical_activity -0.473 0.0301 -15.7 4.02e- 40 -0.532 -0.413
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.64 0.134 34.5 8.47e-102 4.37 4.90
2 social_media_usage -0.00324 0.0550 -0.0590 9.53e- 1 -0.111 0.105
3 physical_activity -0.478 0.0313 -15.3 1.26e- 38 -0.540 -0.417
4 factor(has_siblings)1 0.0822 0.122 0.675 5.00e- 1 -0.157 0.322
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.48 0.955 4.69 0.00000428 2.60 6.36
2 social_media_usage 0.249 0.0737 3.38 0.000825 0.104 0.394
3 age -0.112 0.0697 -1.60 0.111 -0.249 0.0258
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.78 0.952 5.02 0.000000928 2.91 6.65
2 social_media_usage 0.255 0.0729 3.50 0.000548 0.112 0.399
3 age -0.112 0.0690 -1.62 0.107 -0.247 0.0243
4 factor(has_siblings)1 -0.415 0.159 -2.61 0.00951 -0.728 -0.102
# … with abbreviated variable names ¹conf.low, ²conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.20 0.700 8.86 1.04e-16 4.82 7.58
2 social_media_usage 0.0238 0.0552 0.431 6.67e- 1 -0.0849 0.133
3 age -0.112 0.0505 -2.22 2.76e- 2 -0.211 -0.0125
4 physical_activity -0.473 0.0299 -15.8 1.73e-40 -0.531 -0.414
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.16 0.703 8.77 2.02e-16 4.78 7.55
2 social_media_usage 0.0199 0.0556 0.358 7.20e- 1 -0.0895 0.129
3 age -0.112 0.0506 -2.21 2.77e- 2 -0.211 -0.0123
4 physical_activity -0.478 0.0310 -15.4 5.51e-39 -0.539 -0.417
5 factor(has_siblings)1 0.0821 0.121 0.680 4.97e- 1 -0.156 0.320
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0735 40.2 2.01e-117 2.81 3.10
2 social_media_usage 0.0504 0.0737 0.684 4.94e- 1 -0.0946 0.195
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.24 0.138 23.5 1.42e-67 2.97 3.51
2 social_media_usage 0.0338 0.0734 0.460 6.46e- 1 -0.111 0.178
3 factor(has_siblings)1 -0.392 0.163 -2.40 1.70e- 2 -0.712 -0.0707
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.68 0.118 39.6 1.36e-115 4.45 4.91
2 social_media_usage -0.0366 0.0529 -0.691 4.90e- 1 -0.141 0.0676
3 physical_activity -0.475 0.0292 -16.3 3.34e- 42 -0.532 -0.417
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.64 0.133 34.9 8.03e-103 4.38 4.91
2 social_media_usage -0.0342 0.0531 -0.644 5.20e- 1 -0.139 0.0704
3 physical_activity -0.479 0.0301 -15.9 6.71e- 41 -0.539 -0.420
4 factor(has_siblings)1 0.0758 0.121 0.625 5.32e- 1 -0.163 0.315
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.95 0.963 4.10 0.0000534 2.06 5.85
2 social_media_usage 0.0607 0.0743 0.817 0.414 -0.0856 0.207
3 age -0.0730 0.0703 -1.04 0.300 -0.212 0.0655
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.19 0.960 4.36 0.0000181 2.30 6.08
2 social_media_usage 0.0438 0.0740 0.592 0.555 -0.102 0.190
3 age -0.0699 0.0698 -1.00 0.317 -0.207 0.0675
4 factor(has_siblings)1 -0.388 0.163 -2.38 0.0179 -0.709 -0.0675
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.12 0.697 8.79 1.71e-16 4.75 7.50
2 social_media_usage -0.0221 0.0530 -0.417 6.77e- 1 -0.127 0.0823
3 age -0.105 0.0500 -2.10 3.66e- 2 -0.204 -0.00659
4 physical_activity -0.477 0.0290 -16.5 8.94e-43 -0.534 -0.420
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.10 0.699 8.72 2.68e-16 4.72 7.47
2 social_media_usage -0.0194 0.0532 -0.363 7.17e- 1 -0.124 0.0855
3 age -0.106 0.0501 -2.12 3.51e- 2 -0.205 -0.00746
4 physical_activity -0.482 0.0299 -16.1 1.65e-41 -0.541 -0.423
5 factor(has_siblings)1 0.0833 0.121 0.691 4.90e- 1 -0.154 0.321
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0735 40.2 1.85e-117 2.81 3.10
2 social_media_usage 0.0601 0.0736 0.816 4.15e- 1 -0.0849 0.205
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.26 0.138 23.6 4.12e-68 2.99 3.53
2 social_media_usage 0.0795 0.0733 1.08 2.79e- 1 -0.0648 0.224
3 factor(has_siblings)1 -0.417 0.163 -2.56 1.10e- 2 -0.737 -0.0963
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.68 0.118 39.6 1.63e-115 4.45 4.92
2 social_media_usage -0.0385 0.0530 -0.726 4.68e- 1 -0.143 0.0658
3 physical_activity -0.475 0.0292 -16.3 3.60e- 42 -0.533 -0.418
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.64 0.132 35.1 2.57e-103 4.38 4.90
2 social_media_usage -0.0442 0.0535 -0.826 4.09e- 1 -0.150 0.0612
3 physical_activity -0.481 0.0303 -15.9 9.48e- 41 -0.541 -0.422
4 factor(has_siblings)1 0.0953 0.122 0.781 4.36e- 1 -0.145 0.335
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.86 0.955 4.05 0.0000676 1.98 5.74
2 social_media_usage 0.0613 0.0737 0.833 0.406 -0.0837 0.206
3 age -0.0664 0.0697 -0.952 0.342 -0.204 0.0709
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.15 0.952 4.36 0.0000183 2.28 6.03
2 social_media_usage 0.0807 0.0733 1.10 0.272 -0.0636 0.225
3 age -0.0656 0.0690 -0.951 0.343 -0.202 0.0703
4 factor(has_siblings)1 -0.416 0.163 -2.56 0.0111 -0.737 -0.0956
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.16 0.692 8.90 7.81e-17 4.80 7.52
2 social_media_usage -0.0371 0.0526 -0.704 4.82e- 1 -0.141 0.0666
3 age -0.107 0.0496 -2.17 3.12e- 2 -0.205 -0.00975
4 physical_activity -0.478 0.0290 -16.5 7.70e-43 -0.535 -0.421
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.12 0.694 8.82 1.38e-16 4.76 7.49
2 social_media_usage -0.0431 0.0532 -0.810 4.18e- 1 -0.148 0.0616
3 age -0.108 0.0496 -2.18 3.02e- 2 -0.206 -0.0104
4 physical_activity -0.485 0.0301 -16.1 1.95e-41 -0.544 -0.426
5 factor(has_siblings)1 0.100 0.121 0.825 4.10e- 1 -0.139 0.339
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0716 41.3 4.24e-120 2.82 3.10
2 social_media_usage 0.165 0.0421 3.90 1.19e- 4 0.0816 0.248
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.23 0.134 24.1 7.45e-70 2.97 3.50
2 social_media_usage 0.163 0.0418 3.89 1.26e- 4 0.0803 0.245
3 factor(has_siblings)1 -0.386 0.158 -2.44 1.52e- 2 -0.697 -0.0751
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.68 0.124 37.8 5.49e-111 4.44 4.92
2 social_media_usage -0.00578 0.0329 -0.176 8.61e- 1 -0.0705 0.0589
3 physical_activity -0.475 0.0308 -15.4 5.53e- 39 -0.535 -0.414
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.64 0.137 34.0 2.65e-100 4.37 4.91
2 social_media_usage -0.00737 0.0330 -0.223 8.23e- 1 -0.0723 0.0576
3 physical_activity -0.480 0.0319 -15.0 1.15e- 37 -0.543 -0.417
4 factor(has_siblings)1 0.0833 0.121 0.687 4.93e- 1 -0.155 0.322
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.74 0.950 5.00 0.00000104 2.88 6.61
2 social_media_usage 0.183 0.0430 4.24 0.0000299 0.0980 0.267
3 age -0.131 0.0694 -1.89 0.0600 -0.268 0.00556
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.h…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.00 0.947 5.28 2.68e-7 3.13 6.86
2 social_media_usage 0.180 0.0427 4.23 3.21e-5 0.0964 0.264
3 age -0.129 0.0688 -1.88 6.13e-2 -0.265 0.00616
4 factor(has_siblings)1 -0.383 0.157 -2.43 1.55e-2 -0.693 -0.0733
# … with abbreviated variable names ¹conf.low, ²conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.19 0.702 8.82 1.36e-16 4.81 7.57
2 social_media_usage 0.0102 0.0335 0.305 7.60e- 1 -0.0556 0.0761
3 age -0.111 0.0508 -2.19 2.96e- 2 -0.211 -0.0111
4 physical_activity -0.473 0.0306 -15.4 4.12e-39 -0.533 -0.413
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.15 0.705 8.73 2.53e-16 4.77 7.54
2 social_media_usage 0.00863 0.0336 0.257 7.97e- 1 -0.0574 0.0747
3 age -0.111 0.0509 -2.19 2.95e- 2 -0.212 -0.0111
4 physical_activity -0.479 0.0317 -15.1 8.29e-38 -0.541 -0.416
5 factor(has_siblings)1 0.0845 0.120 0.701 4.84e- 1 -0.153 0.322
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0722 40.9 2.91e-119 2.81 3.10
2 social_media_usage 0.0940 0.0289 3.25 1.30e- 3 0.0370 0.151
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.24 0.135 24.0 2.37e-69 2.97 3.51
2 social_media_usage 0.0933 0.0287 3.25 1.28e- 3 0.0369 0.150
3 factor(has_siblings)1 -0.393 0.159 -2.47 1.41e- 2 -0.707 -0.0798
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.70 0.123 38.2 4.18e-112 4.46 4.94
2 social_media_usage -0.0158 0.0222 -0.713 4.76e- 1 -0.0595 0.0279
3 physical_activity -0.480 0.0306 -15.7 4.63e- 40 -0.540 -0.419
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.66 0.136 34.3 3.21e-101 4.39 4.92
2 social_media_usage -0.0170 0.0223 -0.765 4.45e- 1 -0.0609 0.0268
3 physical_activity -0.486 0.0317 -15.3 9.99e- 39 -0.548 -0.423
4 factor(has_siblings)1 0.0884 0.121 0.729 4.67e- 1 -0.150 0.327
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.57 0.956 4.78 0.00000290 2.69 6.45
2 social_media_usage 0.105 0.0295 3.54 0.000462 0.0465 0.163
3 age -0.118 0.0699 -1.69 0.0922 -0.256 0.0195
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.83 0.954 5.07 0.000000750 2.95 6.71
2 social_media_usage 0.104 0.0293 3.55 0.000456 0.0462 0.161
3 age -0.117 0.0692 -1.68 0.0932 -0.253 0.0197
4 factor(has_siblings)1 -0.391 0.159 -2.46 0.0144 -0.704 -0.0786
# … with abbreviated variable names ¹conf.low, ²conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.13 0.701 8.74 2.40e-16 4.75 7.51
2 social_media_usage -0.00615 0.0226 -0.273 7.85e- 1 -0.0506 0.0383
3 age -0.105 0.0507 -2.07 3.95e- 2 -0.205 -0.00507
4 physical_activity -0.479 0.0304 -15.7 3.13e-40 -0.538 -0.419
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.09 0.704 8.65 4.53e-16 4.70 7.47
2 social_media_usage -0.00736 0.0226 -0.325 7.45e- 1 -0.0519 0.0372
3 age -0.105 0.0508 -2.07 3.94e- 2 -0.205 -0.00515
4 physical_activity -0.485 0.0315 -15.4 6.63e-39 -0.547 -0.423
5 factor(has_siblings)1 0.0894 0.121 0.742 4.59e- 1 -0.148 0.327
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0718 41.2 3.30e-120 2.82 3.10
2 social_media_usage 0.254 0.0719 3.53 4.83e- 4 0.112 0.396
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.22 0.135 23.8 5.79e-69 2.95 3.48
2 social_media_usage 0.244 0.0716 3.41 7.51e- 4 0.103 0.385
3 factor(has_siblings)1 -0.358 0.159 -2.25 2.51e- 2 -0.672 -0.0451
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.67 0.122 38.4 1.08e-112 4.43 4.91
2 social_media_usage -0.0183 0.0554 -0.331 7.41e- 1 -0.127 0.0907
3 physical_activity -0.473 0.0303 -15.6 9.46e- 40 -0.533 -0.414
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.64 0.136 34.1 9.26e-101 4.37 4.90
2 social_media_usage -0.0188 0.0554 -0.340 7.34e- 1 -0.128 0.0903
3 physical_activity -0.478 0.0313 -15.3 1.32e- 38 -0.539 -0.416
4 factor(has_siblings)1 0.0753 0.121 0.624 5.33e- 1 -0.162 0.313
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.50 0.947 4.74 0.00000335 2.63 6.36
2 social_media_usage 0.276 0.0730 3.78 0.000191 0.132 0.420
3 age -0.112 0.0692 -1.62 0.105 -0.249 0.0238
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.71 0.946 4.98 0.00000112 2.85 6.57
2 social_media_usage 0.265 0.0726 3.65 0.000309 0.122 0.408
3 age -0.110 0.0687 -1.59 0.112 -0.245 0.0257
4 factor(has_siblings)1 -0.354 0.159 -2.23 0.0267 -0.666 -0.0412
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.16 0.698 8.83 1.29e-16 4.78 7.53
2 social_media_usage 0.00301 0.0559 0.0539 9.57e- 1 -0.107 0.113
3 age -0.109 0.0504 -2.16 3.17e- 2 -0.208 -0.00960
4 physical_activity -0.473 0.0301 -15.7 4.75e-40 -0.532 -0.414
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.13 0.700 8.75 2.20e-16 4.75 7.50
2 social_media_usage 0.00260 0.0560 0.0465 9.63e- 1 -0.108 0.113
3 age -0.109 0.0504 -2.17 3.10e- 2 -0.209 -0.0101
4 physical_activity -0.478 0.0311 -15.4 6.13e-39 -0.539 -0.417
5 factor(has_siblings)1 0.0800 0.120 0.668 5.05e- 1 -0.156 0.316
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0721 41.1 7.92e-120 2.82 3.10
2 social_media_usage 0.232 0.0722 3.22 1.44e- 3 0.0903 0.375
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.26 0.135 24.1 5.88e-70 2.99 3.52
2 social_media_usage 0.239 0.0715 3.35 9.36e- 4 0.0985 0.380
3 factor(has_siblings)1 -0.413 0.159 -2.59 1.00e- 2 -0.726 -0.0994
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.66 0.121 38.7 1.54e-113 4.43 4.90
2 social_media_usage -0.00443 0.0547 -0.0809 9.36e- 1 -0.112 0.103
3 physical_activity -0.471 0.0300 -15.7 3.60e- 40 -0.530 -0.412
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.63 0.134 34.6 3.53e-102 4.36 4.89
2 social_media_usage -0.00831 0.0551 -0.151 8.80e- 1 -0.117 0.100
3 physical_activity -0.476 0.0311 -15.3 1.13e- 38 -0.537 -0.415
4 factor(has_siblings)1 0.0768 0.121 0.632 5.28e- 1 -0.162 0.316
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.49 0.953 4.71 0.00000399 2.61 6.36
2 social_media_usage 0.256 0.0734 3.48 0.000582 0.111 0.400
3 age -0.112 0.0696 -1.61 0.110 -0.249 0.0253
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.79 0.950 5.03 0.000000866 2.91 6.66
2 social_media_usage 0.263 0.0727 3.61 0.000365 0.119 0.406
3 age -0.112 0.0689 -1.62 0.106 -0.248 0.0238
4 factor(has_siblings)1 -0.413 0.159 -2.60 0.00978 -0.725 -0.100
# … with abbreviated variable names ¹conf.low, ²conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.19 0.699 8.85 1.12e-16 4.81 7.56
2 social_media_usage 0.0187 0.0553 0.339 7.35e- 1 -0.0901 0.128
3 age -0.112 0.0505 -2.21 2.80e- 2 -0.211 -0.0121
4 physical_activity -0.471 0.0298 -15.8 1.58e-40 -0.529 -0.412
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.15 0.703 8.75 2.15e-16 4.77 7.53
2 social_media_usage 0.0149 0.0557 0.267 7.90e- 1 -0.0948 0.125
3 age -0.111 0.0505 -2.21 2.82e- 2 -0.211 -0.0120
4 physical_activity -0.476 0.0309 -15.4 5.07e-39 -0.537 -0.415
5 factor(has_siblings)1 0.0766 0.121 0.635 5.26e- 1 -0.161 0.314
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0734 40.4 4.98e-118 2.82 3.10
2 social_media_usage 0.0524 0.0735 0.713 4.76e- 1 -0.0922 0.197
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.24 0.138 23.5 1.09e-67 2.97 3.51
2 social_media_usage 0.0363 0.0732 0.496 6.20e- 1 -0.108 0.180
3 factor(has_siblings)1 -0.385 0.163 -2.37 1.86e- 2 -0.706 -0.0648
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.67 0.117 39.9 1.55e-116 4.44 4.90
2 social_media_usage -0.0374 0.0528 -0.709 4.79e- 1 -0.141 0.0665
3 physical_activity -0.472 0.0289 -16.3 2.24e- 42 -0.529 -0.415
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.63 0.133 35.0 3.02e-103 4.37 4.90
2 social_media_usage -0.0353 0.0530 -0.666 5.06e- 1 -0.140 0.0690
3 physical_activity -0.476 0.0298 -16.0 4.28e- 41 -0.535 -0.418
4 factor(has_siblings)1 0.0691 0.121 0.572 5.68e- 1 -0.169 0.307
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.95 0.963 4.10 0.0000548 2.05 5.84
2 social_media_usage 0.0627 0.0742 0.845 0.399 -0.0833 0.209
3 age -0.0721 0.0703 -1.03 0.306 -0.211 0.0663
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.18 0.960 4.35 0.0000191 2.29 6.07
2 social_media_usage 0.0463 0.0739 0.626 0.532 -0.0992 0.192
3 age -0.0689 0.0697 -0.988 0.324 -0.206 0.0684
4 factor(has_siblings)1 -0.382 0.163 -2.35 0.0196 -0.703 -0.0616
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.12 0.696 8.79 1.69e-16 4.75 7.49
2 social_media_usage -0.0229 0.0529 -0.433 6.66e- 1 -0.127 0.0813
3 age -0.105 0.0500 -2.11 3.58e- 2 -0.204 -0.00707
4 physical_activity -0.475 0.0288 -16.5 5.80e-43 -0.531 -0.418
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.09 0.698 8.73 2.63e-16 4.72 7.47
2 social_media_usage -0.0204 0.0531 -0.384 7.01e- 1 -0.125 0.0842
3 age -0.106 0.0501 -2.13 3.44e- 2 -0.205 -0.00787
4 physical_activity -0.479 0.0297 -16.2 1.02e-41 -0.538 -0.421
5 factor(has_siblings)1 0.0767 0.120 0.639 5.24e- 1 -0.160 0.313
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0733 40.4 3.81e-118 2.82 3.10
2 social_media_usage 0.0782 0.0734 1.07 2.87e- 1 -0.0663 0.223
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.26 0.138 23.7 2.22e-68 2.99 3.53
2 social_media_usage 0.0988 0.0731 1.35 1.78e- 1 -0.0451 0.243
3 factor(has_siblings)1 -0.417 0.163 -2.56 1.09e- 2 -0.737 -0.0969
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.68 0.118 39.8 2.68e-116 4.45 4.91
2 social_media_usage -0.0502 0.0531 -0.946 3.45e- 1 -0.155 0.0543
3 physical_activity -0.474 0.0291 -16.3 2.52e- 42 -0.531 -0.417
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.63 0.132 35.2 7.98e-104 4.37 4.89
2 social_media_usage -0.0565 0.0537 -1.05 2.94e- 1 -0.162 0.0493
3 physical_activity -0.480 0.0302 -15.9 6.75e- 41 -0.540 -0.421
4 factor(has_siblings)1 0.0942 0.122 0.773 4.40e- 1 -0.146 0.334
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.86 0.953 4.05 0.0000667 1.98 5.74
2 social_media_usage 0.0799 0.0734 1.09 0.277 -0.0647 0.224
3 age -0.0659 0.0696 -0.947 0.345 -0.203 0.0711
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.15 0.951 4.37 0.0000179 2.28 6.02
2 social_media_usage 0.100 0.0731 1.37 0.171 -0.0435 0.244
3 age -0.0652 0.0689 -0.946 0.345 -0.201 0.0705
4 factor(has_siblings)1 -0.416 0.163 -2.56 0.0110 -0.737 -0.0962
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.16 0.691 8.91 7.24e-17 4.80 7.52
2 social_media_usage -0.0483 0.0527 -0.917 3.60e- 1 -0.152 0.0555
3 age -0.108 0.0495 -2.17 3.07e- 2 -0.205 -0.0101
4 physical_activity -0.477 0.0289 -16.5 5.28e-43 -0.534 -0.420
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.12 0.693 8.83 1.28e-16 4.75 7.48
2 social_media_usage -0.0549 0.0534 -1.03 3.04e- 1 -0.160 0.0501
3 age -0.108 0.0495 -2.19 2.97e- 2 -0.206 -0.0107
4 physical_activity -0.484 0.0300 -16.1 1.36e-41 -0.543 -0.425
5 factor(has_siblings)1 0.0990 0.121 0.818 4.14e- 1 -0.139 0.337
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0714 41.5 8.06e-121 2.82 3.10
2 social_media_usage 0.167 0.0420 3.99 8.46e- 5 0.0848 0.250
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.24 0.134 24.2 4.03e-70 2.97 3.50
2 social_media_usage 0.166 0.0416 3.99 8.56e- 5 0.0840 0.248
3 factor(has_siblings)1 -0.383 0.158 -2.43 1.58e- 2 -0.694 -0.0727
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.67 0.123 38.0 1.10e-111 4.43 4.91
2 social_media_usage -0.00806 0.0329 -0.245 8.06e- 1 -0.0728 0.0566
3 physical_activity -0.473 0.0307 -15.4 5.00e- 39 -0.533 -0.412
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.63 0.136 34.1 1.12e-100 4.37 4.90
2 social_media_usage -0.00965 0.0330 -0.293 7.70e- 1 -0.0746 0.0553
3 physical_activity -0.478 0.0318 -15.0 1.03e- 37 -0.540 -0.415
4 factor(has_siblings)1 0.0774 0.121 0.640 5.23e- 1 -0.161 0.316
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.75 0.948 5.01 0.000000964 2.89 6.62
2 social_media_usage 0.186 0.0428 4.33 0.0000208 0.101 0.270
3 age -0.131 0.0693 -1.89 0.0592 -0.268 0.00512
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.h…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.00 0.946 5.29 2.50e-7 3.14 6.86
2 social_media_usage 0.184 0.0425 4.33 2.12e-5 0.100 0.267
3 age -0.129 0.0687 -1.89 6.04e-2 -0.265 0.00567
4 factor(has_siblings)1 -0.380 0.157 -2.42 1.61e-2 -0.689 -0.0710
# … with abbreviated variable names ¹conf.low, ²conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.18 0.701 8.81 1.43e-16 4.80 7.56
2 social_media_usage 0.00793 0.0335 0.237 8.13e- 1 -0.0579 0.0738
3 age -0.111 0.0508 -2.18 2.99e- 2 -0.211 -0.0109
4 physical_activity -0.471 0.0305 -15.4 3.79e-39 -0.531 -0.411
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.14 0.704 8.72 2.65e-16 4.76 7.53
2 social_media_usage 0.00634 0.0336 0.189 8.50e- 1 -0.0598 0.0724
3 age -0.111 0.0509 -2.18 2.98e- 2 -0.211 -0.0109
4 physical_activity -0.476 0.0316 -15.1 7.62e-38 -0.538 -0.414
5 factor(has_siblings)1 0.0784 0.120 0.653 5.14e- 1 -0.158 0.315
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 2 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.96 0.0719 41.2 4.98e-120 2.82 3.10
2 social_media_usage 0.0966 0.0285 3.39 8.04e- 4 0.0405 0.153
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.24 0.135 24.0 1.15e-69 2.98 3.51
2 social_media_usage 0.0964 0.0283 3.41 7.46e- 4 0.0407 0.152
3 factor(has_siblings)1 -0.391 0.159 -2.46 1.45e- 2 -0.704 -0.0781
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.69 0.122 38.4 1.09e-112 4.45 4.93
2 social_media_usage -0.0188 0.0220 -0.853 3.94e- 1 -0.0622 0.0246
3 physical_activity -0.479 0.0305 -15.7 4.31e- 40 -0.539 -0.419
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.h…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.65 0.135 34.4 1.42e-101 4.39 4.92
2 social_media_usage -0.0201 0.0221 -0.908 3.65e- 1 -0.0637 0.0235
3 physical_activity -0.484 0.0316 -15.3 9.32e- 39 -0.547 -0.422
4 factor(has_siblings)1 0.0840 0.121 0.695 4.88e- 1 -0.154 0.322
# … with abbreviated variable name ¹conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 3 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.59 0.954 4.81 0.00000254 2.71 6.46
2 social_media_usage 0.107 0.0291 3.69 0.000273 0.0500 0.165
3 age -0.119 0.0697 -1.71 0.0888 -0.256 0.0182
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.…¹ conf.…²
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.85 0.952 5.09 0.000000652 2.97 6.72
2 social_media_usage 0.107 0.0288 3.71 0.000253 0.0501 0.164
3 age -0.118 0.0691 -1.70 0.0894 -0.254 0.0182
4 factor(has_siblings)1 -0.389 0.158 -2.46 0.0147 -0.700 -0.0771
# … with abbreviated variable names ¹conf.low, ²conf.high
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.11 0.700 8.72 2.63e-16 4.73 7.49
2 social_media_usage -0.00924 0.0224 -0.413 6.80e- 1 -0.0533 0.0349
3 age -0.104 0.0507 -2.05 4.12e- 2 -0.204 -0.00418
4 physical_activity -0.478 0.0303 -15.7 3.12e-40 -0.537 -0.418
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
# A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.07 0.703 8.63 4.99e-16 4.69 7.45
2 social_media_usage -0.0105 0.0225 -0.469 6.40e- 1 -0.0548 0.0337
3 age -0.104 0.0507 -2.05 4.12e- 2 -0.204 -0.00419
4 physical_activity -0.483 0.0314 -15.4 6.63e-39 -0.545 -0.421
5 factor(has_siblings)1 0.0847 0.120 0.705 4.81e- 1 -0.152 0.321
model.results = broom::tidy(fit, conf.int = TRUE)
model.results
The estimated effect of social_media_usage on
depression is shown in the figure below, along with other
estimated coefficients. If the coefficient was negative, it would imply
that increased social media usage is inversely associated with
depression i.e. social media usage has a positive impact on mental well
being; on the other hand if it is positive, it would imply that social
media usage is positively associated with depression i.e. social media
usage has a negative impact on mental well being. Finally, if the
coefficient is very close to zero, it suggests that the the relationship
is weak.
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() + geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) + xlim(c(-2, 2)) + ylab("Modelcoefficients") +
theme_minimal() + theme(axis.title.x = element_blank())
model.results %>%
filter(term != "(Intercept)") %>%
ggplot() +
geom_pointrange(aes(y = term, x = estimate, xmin = conf.low, xmax = conf.high)) +
geom_vline(aes(xintercept = 0)) +
xlim(c(-2, 2)) +
ylab("Model coefficients") +
theme_minimal() +
theme(
axis.title.x = element_blank()
)
From the variation in the result, as shown in Figure 1, the impact of social media usage on depression appears to vary based on which choices we make in the data analysis process. This is more evident from the specification curve plot (Figure 2) which shows the variation in the outcome, with only less than half the specifications suggesting a positive effect.
data.spec_curve = extract_variables(M, model.results) %>%
unnest(model.results) %>%
# filter(stringr::str_detect(term, "^I")) %>%
filter(term == "social_media_usage") %>%
select( .universe, !! names(parameters(M)), estimate, p.value, conf.low, conf.high ) %>%
arrange( estimate ) %>%
mutate(
.universe = 1:nrow(.),
effect = ifelse(p.value < 0.05, ifelse(estimate < 0, "negative", "positive"), "not significant")
)
p1 <- data.spec_curve %>%
gather( "parameter_name", "parameter_option", !! names(parameters(M)) ) %>%
mutate( parameter_name = factor(stringr::str_replace(parameter_name, "_", "\n")) ) %>%
ggplot() +
geom_point( aes(x = .universe, y = parameter_option, color = effect), size = 1 ) +
labs( x = "universe #", y = "option included in the analysis specification") +
facet_grid(parameter_name ~ ., space="free_y", scales="free_y", switch="y")+
scale_colour_manual(values=c("#FF684B", "#999999", "#6E52EB")) +
theme_minimal() +
theme(strip.placement = "outside",
strip.background = element_rect(fill=NA,colour=NA),
panel.spacing.x=unit(0.15,"cm"),
strip.text.y = element_text(angle = 180, face="bold", size=10),
panel.spacing = unit(0.25, "lines")
)
p2 <- data.spec_curve %>%
ggplot() +
ggdist::geom_pointinterval(aes(x = .universe, y = estimate, ymin = conf.low, ymax = conf.high, color = effect)) +
labs(x = "", y = "effect size") +
theme_minimal() +
scale_colour_manual(values=c("#FF684B", "#999999", "#6E52EB"))
cowplot::plot_grid(p2, p1, axis = "bltr", align = "v", ncol = 1, rel_heights = c(1, 3))
## Warning: Using the `size` aesthietic with geom_segment was deprecated in ggplot2 3.4.0.
## ℹ Please use the `linewidth` aesthetic instead.
As a result, we conclude that the association between social media usage and depression is not robust to arbitrary choices in the data analysis process, and any impact that prior work has found on social media usage on depression is likely due to idiosyncratic choices in the data analysis process.